Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) with generalized extreme value (GEV) distribution to forecast the block maximum value of a time series. Implementing such a network is a challenge as the framework must preserve the inter-dependent constraints among the GEV model parameters even when the DNN is initialized. We describe our approach to address this challenge and present an architecture that enables both conditional mean and quantile prediction of the block maxima. The extensive experiments performed on both real-world and synthetic data demonstrated the superiority of DeepExtrema compared to other baseline methods.
Monash University（莫纳什大学）；University of Sydney（悉尼大学）
Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work. In this research, we first introduce a notion of static and dynamic features, which then enables us to develop our autonomous Feature Retrieving Autoregressive Network for Static features (FRANS) that does not require domain knowledge. The method is based on a CNN classifie…
WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
NIT Meghalaya（梅加拉亚国家理工学院）；Sorbonne University（索邦大学）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；Universiti Kebangsaan（马来西亚国民大学）；University of Philippines（菲律宾大学）；Institute of Environmental Science and Meteorology（环境科学与气象研究所）
Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
East China Normal University（东华大学）；Nanyang Technological University（南洋理工大学）；Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China（电子科技大学）
Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Teórica y Computacional（阿根廷国家科学研究与技术理事会（CONICET）、科里亚物理化学研究所（INFIQC）、科里亚国立大学、化学科学学院、理论与计算化学系）
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
International Institute of Information Technology Bangalore（班加罗尔国际信息技术学院）；Sorbonne University Abu Dhabi（索邦大学阿布扎比分校）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；National University of Singapore（新加坡国立大学）
Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
Federal Office of Meteorology and Climatology MeteoSwiss（瑞士联邦气象与气候办公室（MeteoSwiss））；Swiss Data Science Center (SDSC), ETH Zürich（苏黎世联邦理工学院瑞士数据科学中心（SDSC））；Center for Climate Systems Modeling (C2SM), ETH Zürich（苏黎世联邦理工学院气候系统建模中心（C2SM））；European Centre for Medium-Range Weather Forecasts (ECMWF)（欧洲中期天气预报中心（ECMWF））；Norwegian Meteorological Institute (MET Norway)（挪威气象研究所（MET Norway））；Royal Netherlands Meteorological Institute (KNMI)（荷兰皇家气象研究所（KNMI））；Institute for Atmospheric and Climate Science (IAC), ETH Zürich（苏黎世联邦理工学院大气与气候科学研究所（IAC））
The technology of traffic flow forecasting plays an important role in intelligent transportation systems. Based on graph neural networks and attention mechanisms, most previous works utilize the transformer architecture to discover spatiotemporal dependencies and dynamic relationships. However, they have not considered correlation information among spatiotemporal sequences thoroughly. In this paper, based on the maximal information coefficient, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr). Using SC…
Universal Time Series Generation with Neural Controlled Differential Equations
Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；IAR, Karlsruhe Institute of Technology（卡尔斯鲁厄技术大学IAR部门）；Max Planck Institute for Polymer Research（马克斯·普朗克聚合物研究所）；IWR, Heidelberg University（海德堡大学IWR部门）；Dept. of Computer Science, University of Toronto（多伦多大学计算机科学系）；Mathematical Institute, University of Oxford（牛津大学数学研究所）；Vector Institute, Toronto, Canada（多伦多向量研究所）
Center for Mathematical Modeling and Department of Mathematical Engineering Universidad de Chile（智利大学数学建模中心和数学工程系）；Center for Mathematical Modeling Universidad de Chile（智利大学数学建模中心）
Gaussian processes (GPs) are Bayesian nonparametric generative models that provide interpretability of hyperparameters, admit closed-form expressions for training and inference, and are able to accurately represent uncertainty. To model general non-Gaussian data with complex correlation structure, GPs can be paired with an expressive covariance kernel and then fed into a nonlinear transformation (or warping). However, overparametrising the kernel and the warping is known to, respectively, hinder gradient-based training and make the predictions computationally expensive. We remedy this issue by…
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the framework of contrastive learning with the time-based augmentation techniques to sample positives and negatives for contrastive training. Nevertheless, they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context. Besides, they all pay no attention to incorporate the spectral information in fea…
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system. In many of these applications, multiple periodic components exist and are often interlaced with each other. Such dynamic and complicated periodic patterns make the accurate periodicity detection difficult. In addition, other components in the time series, such as trend, outliers and noises, also pose additional challenges for accurate periodicity detection. In this paper, we propose a robust and genera…
This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of…
Enhanced LFTSformer: A Novel Long-Term Financial Time Series Prediction Model Using Advanced Feature Engineering and the DS Encoder Informer Architecture
School of Mathematica, Shanghai University of Finance and Economics（上海财经大学数学学院）；Faculty of Electronic and Information Engineering, Xi'an Jiaotong University（西安交通大学电子与信息工程学院）；School of Computing Information System, The University of Melbourne（墨尔本大学计算信息系统学院）
